Bibliographic record
Abstract
The hypothesis that females prefer older males because they have higher mean fitness than younger males has been the centre of recent controversy. These discussions have focused on the success of a female who prefers males of a particular age class when age cues, but not quality cues, are available. Thus, if the distribution of male quality changes with age, such that older males have on average genotypes with higher fitness than younger males, then a female who mates with older males has fitter offspring, which allows the female preference to spread through a genetic correlation. We develop a general model for male display in a species with multiple reproductive bouts that allows us to identify the conditions that promote reliable signalling within an age class. Because males have opportunities for future reproduction, they will reduce their levels of advertising compared with a semelparous species. In addition, because higher-quality males have more future reproduction, they will reduce their advertising more than low-quality males. Thus, the conditions for reliable signalling in a semelparous organism are generally not sufficient to produce reliable signalling in species with multiple reproductive bouts. This result is due to the possibility of future reproduction so that, as individuals age and the opportunities for future reproduction fade, signalling becomes more reliable. This provides a novel rationale for female preference for older mates; older males reveal more information in their sexual displays.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".